Recently, various techniques have been proposed to automatically provide personalized feedback on programming exercises. The cutting edge of which is the data-driven approaches that leverage a corpus of existing correct programs and repair incorrect submissions by using similar reference programs in the corpus. However, current data-driven techniques work under the strong assumption that the corpus contains a solution program that is close enough to the incorrect submission.
In this paper, we present $\textsc{Cafe}$, a new data-driven approach for feedback generation that overcomes this limitation. Unlike existing approaches, $\textsc{Cafe}$ uses a novel context-aware repair algorithm that can generate feedback even if the incorrect program differs significantly from the reference solutions. We implemented $\textsc{Cafe}$ for OCaml and evaluated it with 4,211 real student programs. The results show that $\textsc{Cafe}$ is able to repair 83% of incorrect submissions, far outperforming existing approaches.